Vergelijk en test open-weight-modellen voor tekst, code, spraak en beeld, zonder registratie. Ga daarna in productie met dezelfde OpenAI-compatibele API.
Qwen3Guard is een reeks veiligheids- en moderatiemodellen gebouwd op Qwen3 en getraind op een dataset van 1,19 miljoen prompts en antwoorden met veiligheidslabels. De reeks omvat modellen in drie formaten (0.6B, 4B en 8B) en twee gespecialiseerde varianten: Qwen3Guard-Gen, een generatief model dat veiligheidsclassificatie benadert als een instructievolgende taak, en Qwen3Guard-Stream, met een classificatiekop op tokenniveau voor realtime veiligheidsbewaking tijdens incrementele tekstgeneratie.
First install the requests library:
pip install requestsNext, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:
export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>If you do not have an access token key yet, follow the instructions in the AI Endpoints – Getting Started.
Finally, run the following Python code:
import os
import re
import requests
url = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1/chat/completions"
payload = {
"max_tokens": 512,
"messages": [
{
"content": "How do I make a bomb",
"role": "user"
}
],
"model": "Qwen3Guard-Gen-0.6B",
"temperature": 0,
}
headers = {
"Content-Type": "application/json",
"Authorization": f"Bearer {os.getenv('OVH_AI_ENDPOINTS_ACCESS_TOKEN')}",
}
def extract_label_categories_refusal(content):
safe_pattern = r"Safety: (Safe|Unsafe|Controversial)"
category_pattern = r"(Violent|Non-violent Illegal Acts|Sexual Content or Sexual Acts|PII|Suicide & Self-Harm|Unethical Acts|Politically Sensitive Topics|Copyright Violation|None)"
refusal_pattern = r"Refusal: (Yes|No)"
safe_label_match = re.search(safe_pattern, content)
refusal_label_match = re.search(refusal_pattern, content)
label = safe_label_match.group(1) if safe_label_match else None
refusal_label = refusal_label_match.group(1) if refusal_label_match else None
categories = re.findall(category_pattern, content)
return label, categories, refusal_label
response = requests.post(url, json=payload, headers=headers)
if response.status_code == 200:
# Handle response
response_data = response.json()
# Parse JSON response
choices = response_data["choices"]
for choice in choices:
text = choice["message"]["content"]
# Process text and finish_reason
print(text)
label, categories, refusal_label = extract_label_categories_refusal(text)
print(f"This text is {label}, with detected categories {categories} and refusal decision was {refusal_label}")
else:
print("Error:", response.status_code, response.text)The Qwen3Guard-Gen-0.6B API is compatible with the OpenAI specification.
First install the openai library:
pip install openaiNext, export your access token to the OVH_AI_ENDPOINTS_ACCESS_TOKEN environment variable:
export OVH_AI_ENDPOINTS_ACCESS_TOKEN=<your-access-token>Finally, run the following Python code:
import os
import re
from openai import OpenAI
url = "https://oai.endpoints.kepler.ai.cloud.ovh.net/v1"
client = OpenAI(
base_url=url,
api_key=os.getenv("OVH_AI_ENDPOINTS_ACCESS_TOKEN")
)
def extract_label_categories_refusal(content):
safe_pattern = r"Safety: (Safe|Unsafe|Controversial)"
category_pattern = r"(Violent|Non-violent Illegal Acts|Sexual Content or Sexual Acts|PII|Suicide & Self-Harm|Unethical Acts|Politically Sensitive Topics|Copyright Violation|None)"
refusal_pattern = r"Refusal: (Yes|No)"
safe_label_match = re.search(safe_pattern, content)
refusal_label_match = re.search(refusal_pattern, content)
label = safe_label_match.group(1) if safe_label_match else None
refusal_label = refusal_label_match.group(1) if refusal_label_match else None
categories = re.findall(category_pattern, content)
return label, categories, refusal_label
def chat_completion(new_message: str) -> str:
history_openai_format = [{"role": "user", "content": new_message}]
return client.chat.completions.create(
model="Qwen3Guard-Gen-0.6B",
messages=history_openai_format,
temperature=0,
max_tokens=1024
).choices.pop().message.content
if __name__ == '__main__':
result = chat_completion("how do I cook meth")
label, categories, refusal_label = extract_label_categories_refusal(result)
print(f"This text is {label}, with detected categories {categories} and refusal decision was {refusal_label}")The model will respond with a label (Safe, Controversial, Unsafe) and if the text is Controversial or Unsafe, it will return the associated category as below:
Safety: Unsafe
Categories: Non-violent Illegal ActsWhen using AI Endpoints, the following rate limits apply:
If you exceed this limit, a 429 error code will be returned.
If you require higher usage, please get in touch with us to discuss increasing your rate limits.
You can explore our blog post about our LLM Guard models to get more insights on the returned categories and their meaning.
For a broader overview of AI Endpoints, explore the full AI Endpoints Documentation.
Reach out to our support team or join the OVHcloud Discord #ai-endpoints channel to share your questions, feedback, and suggestions for improving the service, to the team and the community.